Dinh/Dinh 2014/NOTES/2014-1-23

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Revision as of 23:35, 24 January 2014 by >Dinh (→‎Count locus alleles)
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Haplotype Analysis

SAM record parser

  • In order to perform haplotype analysis, I needed to write a SAM record parser so that I can analyze each read and determine the CG positions and their methylation status. This works like samtools pileup but since the current version of samtools mpileup requires a bam file as input and a reference fasta file as input, it is probably more efficient to skip using samtools mpileup.
  • The SAM parser needs to identify the position of each read base, and the mismatch positions. Since there are INDELS and H/S clippings, it needs to integrate both the CIGAR and MD:Z fields.
  • Script for finding mismatch positions in SAM file: File:DdSAMparse.txt
  • Script for counting the CpG locus alleles (also makes BED): File:CgLocusAlleles.txt
  • To compare the parsing results, use sam parser to generate a BED format file for CpG positions & compare with results obtained from samtools pileup -> methylFreq -> BED
NP-GP1V6-Ind1-16-Dec26-Ind10_S10.BED.txt : cor = 0.9999383
NP-GP1V6-Ind1-16-Dec26-Ind11_S11.BED.txt : cor = 0.9999235
NP-GP1V6-Ind1-16-Dec26-Ind12_S12.BED.txt : cor = 0.9999451
NP-GP1V6-Ind1-16-Dec26-Ind13_S13.BED.txt : cor = 0.9999285
NP-GP1V6-Ind1-16-Dec26-Ind14_S14.BED.txt : cor = 0.9998338
NP-GP1V6-Ind1-16-Dec26-Ind15_S15.BED.txt : cor = 0.9999977
NP-GP1V6-Ind1-16-Dec26-Ind16_S16.BED.txt : cor = 0.9999913
NP-GP1V6-Ind1-16-Dec26-Ind9_S9.BED.txt   : cor = 0.9999657
  • There are some positions where in the SAM record parsing produced slightly different results from samtools pileup.
An example of non-concordance call:
samtools pileup: 
chr9	100610863	100610864	'9/19'	19	+	100610863	100610864	90,150,0
sam parser: 
chr9	100610863	100610864	'9/49'	49	+	100610863	100610864	30,210,0
  • Fixed an error with extractMethyl.pl, the Regex for indels can't be used to remove them.
 Regex for indels: \+[0-9]+[ACGTNacgtn]+' \-[0-9]+[ACGTNacgtn]+' 
 Example:
 ...TTTT.T.T..TTT-1GTT-1GT-1GTTTTT-1GTTTTT-1GT-1GTT-1GTTTTTTTTTTTTTT..T
                 ---------------------------------------------------
                 ***  *** ***     ***     *** ***  *** 
 The positions marked with dashes at the bottom were removed using the regex for indels.
 However, only the positions marked with stars should be removed.
  • Also, samtools pileup always convert 64 to 33 (even when the base quality is already in 33).
  • Re-generate BED files from extractMethyl & calculated the correlation between the two

Count locus alleles

  • Count the different alleles produced by four consecutive CpGs on the same read. There are 16 different possibilities.
  • Examples of the locus count:
chr1:1475064:1475086:1475088:1475095    2UMMM,1MMUM,28MMMM,     31
chr1:1475086:1475088:1475095:1475100    1MUMM,30MMMM,   31
chr1:1475095:1475100:1475108:1475130    32MMMM, 32
chr1:1475108:1475130:1475141:1475143    1MMUM,1MMMU,30MMMM,     32
chr1:1475130:1475141:1475143:1475162    1MMUM,1MMMU,29MMMM,     31
chr1:1475141:1475143:1475162:1475164    1MUMM,1MMUM,29MMMM,     31
  • For any read, calculate the probability of seeing a particular allele at a locus in blood sample:
  • Example 1:
 M01186:73:000000000-A20K5:1:1101:15873:5723:1:N:0:3_AGCCTT:R
 chr4:172734347:172734362:172734393:172734395  UUMM  0
 chr4:172734362:172734393:172734395:172734426  UMMM  0
 chr4:172734393:172734395:172734426:172734431  MMMU  0
 chr4:172734395:172734426:172734431:172734435  MMUM  0
 chr4:172734426:172734431:172734435:172734442  MUMU  0
 chr4:172734431:172734435:172734442:172734445  UMUM  0
 chr4:172734435:172734442:172734445:172734453  MUMM  0
 chr4:172734442:172734445:172734453:172734455  UMMM  0
 chr4:172734445:172734453:172734455:172734459	MMMM  0
 Average probability score is 0, meaning that this read have no chance of being originated from a blood sample. (This is a non-blood read).
  • Example 2:
 M01186:73:000000000-A20K5:1:2102:6845:17522:1:N:0:3_CCTTAG:F	  
 chr2:26407679:26407682:26407684:26407689, UUUU
 chr2:26407682:26407684:26407689:26407691, UUUU
 chr2:26407684:26407689:26407691:26407697, UUUU
 chr2:26407689:26407691:26407697:26407713, UUUU
 chr2:26407691:26407697:26407713:26407721, UUUU
 chr2:26407697:26407713:26407721:26407723, UUUU
 chr2:26407713:26407721:26407723:26407725, UUUU
 chr2:26407721:26407723:26407725:26407728, UUUU
 chr2:26407723:26407725:26407728:26407732, UUUU
 chr2:26407725:26407728:26407732:26407741, UUUU
 chr2:26407728:26407732:26407741:26407743, UUUU
 chr2:26407732:26407741:26407743:26407749, UUUU
 chr2:26407741:26407743:26407749:26407762, UUUU
 chr2:26407743:26407749:26407762:26407764, UUUU
 chr2:26407749:26407762:26407764:26407768, UUUU
 chr2:26407762:26407764:26407768:26407773, UUUU
 chr2:26407764:26407768:26407773:26407779, UUUU
 chr2:26407768:26407773:26407779:26407782, UUUU
 0.966,0.967,0.975,0.975,0.979,0.982,0.980,0.979,0.971,0.966,0.968,0.971,0.977,0.984,0.982,0.980,0.971,0.973
 The average probability is high, meaning that his read might have also originated from a blood sample. (This cannot be distinguished from blood).
  • It is almost certain that 0 average scores are not from blood DNA, but it is harder to determine whether a 0.2 or even a 0.9 average score are not from blood. We may improve the confidence by increasing the haplotype size to include more than 4 consecutive CpG sites.
  • I randomly sampled 1/1000 of each cancer BAM files (for read 1 of V4 capture ONLY), and calculate the probability score of finding each read in a blood sample (Indx6). As a control, I sampled 1/1000 of another blood sample to compare.
MaxScoreFilter BLOOD BE2C U87MG BXPC3 PANC1 T98G
0 0 45 44 108 52 71
0.1 2 171 201 457 202 200
0.15 3 194 225 515 215 221
0.2 3 224 246 552 243 254
Total Reads Analyzed 267 576 475 899 365 402

Calculate the r2 between pairs of CpGs